Token Pulse

Token Pulse tracks on-chain holder behavior and exchange flows in near real-time. L1 projects, token teams, and traders use it to see what's happening with a token before the price reacts. Built on top of the Web3 API real-time infrastructure that already processes data across 10+ chains.

The problem

Price charts tell you what already happened. Before a token dumps, big holders start moving to exchanges. Exchange balances spike. Wallets that had been accumulating for months go quiet. All of this is visible on-chain, but it's only useful if you can process it fast enough to act on it. Most people stare at TradingView, watch the candle turn red, and then scramble to figure out what happened.

We wanted to flip that. Show the exchange inflows as they happen, the whale movements in near real-time, and let the price catch up to what the chain already told you.

The product

Three features, each harder to build than it sounds.

• Holder segmentation. We classify wallets by how they've actually traded over time, not just what they hold right now. Some wallets that look small on paper trade like institutions. Some massive wallets are just exchange hot wallets doing routine rebalancing. Getting that classification right was one of the hardest parts of the project.
• Exchange flow detection. Tokens flowing to exchanges usually means selling pressure. Tokens flowing out, accumulation. Simple in theory, but exchange hot wallets rebalance constantly for purely operational reasons. Filtering those from real directional signals took three full iterations.
• Sentiment signals. Are holders bridging to other chains? Buying dips? Staking instead of selling? These patterns reveal whether holders are committed or quietly looking for the exit.

L1 projects use it to monitor token distribution and staking health. Token teams get early warning when distribution patterns shift. Traders get ahead of the charts. Market makers anticipate liquidity needs before the order book reflects them.

The architecture

The data foundation comes from the Web3 API infrastructure, which already handles real-time blockchain data capture, event streaming, and multi-chain normalization. On top of that, we built a streaming pipeline specifically for holder and token analytics. The pipeline processes events from multiple chains and exchanges, runs them through our classification formulas, and exposes everything through a REST API. Sub-second latency for the critical paths, for example when a whale moves tokens, you know within 800ms.

The wallet classification was the most interesting engineering challenge. Balance thresholds don't work. A wallet accumulating slowly over 6 months behaves completely differently than one that just received a large transfer from an exchange. We built statistical formulas that score wallets based on historical behavior patterns across multiple chains. No ML, just math that stays consistent and explainable.

What I learned

Most on-chain activity is routine and meaningless for directional analysis. When we started processing raw chain data, everything looked like a signal. Every large transfer seemed important, every exchange deposit felt bearish. Learning to aggressively filter noise without accidentally discarding real signals took months of iteration and plenty of false positives.

Exchange flow detection turned out to be the most deceptively hard feature. Exchanges move tokens constantly for internal operations: wallet rotations, cold storage rebalancing, liquidity provisioning. We ended up building behavioral profiles for exchange wallets themselves, treating exchanges as entities with predictable patterns rather than just labeled addresses.

We initially targeted 5-second latency and thought that was fast enough. When we pushed it below one second, usage patterns shifted completely. Users went from checking dashboards periodically to keeping them open continuously, treating Token Pulse more like a live feed than a reporting tool. That shift in user behavior justified the cost of maintaining real-time infrastructure.